Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets
Inference is typically intractable in high-treewidth undirected graphical models, making maximum likelihood learning a challenge. One way to overcome this is to restrict parameters to a tractable set, most typically the set of tree-structured parameters. This paper explores an alternative notion of a tractable set, namely a set of "fast-mixing parameters" where Markov chain Monte Carlo (MCMC) inference can be guaranteed to quickly converge to the stationary distribution. While it is common in...[Show more]
|Collections||ANU Research Publications|
|Source:||Reflection, Refraction and Hamiltonian Monte Carlo|
|01_Domke_Maximum_Likelihood_Learning_2015.pdf||269.69 kB||Adobe PDF||Request a copy|
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